The present invention relates to image processing systems generally. More specifically, the present invention relates to improved digital image processing through the removal of non-region of interest information.
Computed Radiography (CR) has gained worldwide acceptance in radiology departments. CR not only provides digital radiographic image data for easy communication and storage, but also produces images with a wide dynamic range that is suitable for various exposure and diagnostic conditions. For a specific exam, however, a CR image needs to be processed digitally to show anatomical information and enhanced diagnostic details. Besides the anatomical portion of a CR image under exam, the CR image often contains collimators of different geometry and thickness such as patient monitoring devices, clothing, and/or image markers. Because the gray-level distributions of these non-anatomical objects often overlap with the gray-level distribution of the anatomy, an automatic image processing scheme may not produce the desired image quality. In addition, a CR image may also include multiple exams or views that are separated by collimators and each exam may have different gray-level distributions.
CR uses photostimulable phosphor imaging plates (IP) and associated hardware and software to acquire and display x-ray projection images. A CR system provides a significant advantage over conventional screen film system in terms of the exposure latitude (about 10,000:1) or wide dynamic range. However, the image contrast produced by a CR system is low and digital image processing is required to extract the diagnostic information from the image data by enhancing image contrast. Since the acquisition and display are separate processes in a CR system, different image processing techniques can be applied to correct for under- or over-exposures and to enhance image quality.
If an IP contains only anatomy, referred to as diagnostic regions of interest (ROI), standard image processing may be applied to produce desired image quality in an automated fashion. However, in computed radiography imaging, collimation is frequently employed to shield irrelevant body parts (i.e., not of interest) from radiation exposure as well as to present radiation scattering from x-ray opaque materials. Collimators are also applied to partition an IP plate into different views so that multiple exams can be exposed on the same IP plate. A view refers to a region on an IP plate that is not covered by the collimators. If an image contains only one exam, the view is the entire uncollimated region. If an IP plate contains more than one exam, the views are the regions that are partitioned by the collimators but not covered by any collimator. Besides collimated regions, a CR image may also contain direct exposure (DE) region, which is a region that has been directly exposed to the x-ray source without attenuation by, for example, collimators, anatomy, or markers, hardware devices, and so on. Therefore, a CR image may contain one or more collimated regions, one or more DE regions, and one or more ROIs.
In an ideal condition, each collimated region contains high intensity pixels with a uniform distribution and the DE regions contain low intensity pixels with a uniform distribution. Therefore, the collimated and the DE regions could be easily identified.
In a clinical setup, however, the ideal imaging setting is often not achievable and the three physically distinct regions (collimated, ROI and DE) can overlap in their distributions. FIG. 1 shows a typical prior art single view CR image 100 having collimated regions 112, ROI 114, and DE regions 116. Some pixels in collimated region 112 have lower intensity (i.e., are darker) than some pixels in the ROI 114 due to use of relatively thin collimators. Furthermore, some pixels in the DE region 116 may have higher intensity (i.e., are lighter) than the pixels in the ROI 114 due to the presence of objects 124 captured within the view, such as hardware devices for patient monitoring, cloth, air pockets, markers, hardware, and/or radiation scattering. Additionally, when a CR image has a view which is significantly skewed, processing of the ROI is extremely difficult. To compound matters, collimated and DE regions provide no useful diagnostic information and make it difficult to produce a resulting high quality, post processing ROI image, even with the use of sophisticated image processing algorithms.
In the case of an IP with multiple views, such as the two human foot views 202, 204 of CR image 200 of prior art FIG. 2, a CR image will typically contain more collimated regions 206, DE regions 208 and ROIs 210 than a single view CR image. That is, each view will contain ROI 210 and DE regions 208 bounded by collimated regions 206. Because the exposure conditions and body parts for the views may vary from one exam (i.e., view) to another, the image enhancement of the CR image containing multiple views is complex. In such cases, all views of different exams need to be identified so that special image processing algorithms can be applied to each view to achieve reasonably good image quality. This processing can require human intervention and take a relatively long amount of time (e.g., several minutes or more).
The present invention is a region of interest (ROI) segmentation system and method that facilitate the isolation of ROI from other data within a digital image. The digital image may be any known type of digital image, such as a computed radiography (CR), digital radiology (DR), digital fluoroscopy (DF), nuclear medicine (NM), computer topography (CT), ultrasound, magnetic resonance (MR), or some other form of digital image. The ROI segmentation system accepts as an input a digital image that contains one or more views, wherein each view within a digital image corresponds to a different exposure. An input digital image of about 8 megabytes (and about 2000xc3x972000 pixels) is typically processed in about 3-5 seconds, while larger images may take longer. Preferably, the input digital image includes at least about 10 percent of ROI. As an output, the ROI segmentation system produces a mask that allows generation of an image substantially void of all ancillary (i.e., non-ROI) information from each view of the original input digital image, wherein direct exposure (DE) regions such as markers and hardware devices and any collimated regions are clearly distinguished.
The ROI segmentation system includes a collimation subsystem configured to detect and remove collimated regions from an input digital image using, for the most part, boundary recognition algorithms. A collimation pre-processor module quickly detects sharp edges of collimated regions and well-defined hardware and marker images. The input image is sub-sampled using bilinear interpolation to produce a sampled digital image of about 2 MB and 1000xc3x971000 pixels.
For edges that are less well-defined, a primary processor module accomplishes a more rigorous analysis. The collimation subsystem primary processor divides the sub-sampled digital image and averages a number of rows and columns to produce 1-dimensional averaged data. An array of the accumulated edge strength data is computed from each of the averaged row or column data. The primary processor processes each of the averaged row and column data and the edge strength data top-to-bottom and bottom-to-top and right-to-left and left-to-right to, ultimately, generate change in pixel intensity information. Using this information, the collimation subsystem primary processor determines most remaining collimated regions.
If collimated regions remain, a secondary processor which is configured to perform even greater analysis using a Hough transform-based process is implemented. Such a process may be necessary, for example, when a collimated edge is at an angle greater than 7 degrees with respect to its corresponding IP plate boundary or when the intensity distribution of the collimated regions overlaps with the other regions. The Hough transform process is implemented on edge gradient data produced by the primary processor, wherein resulting high intensity points in Hough space correspond to collimator edges.
A DE subsystem detects and removes DE regions from the input digital image, by adding them to a mask. A DE pre-processor sub-samples the input digital image and the image is smoothed with a set of gray-level morphological filters with different structural elements to remove some small objects, such as markers, tags, letters, and other hardware items. All of the views of the image are normalized by re-mapping the pixel intensities in each view to occupy a fixed intensity range determined as a function of a corresponding histogram of each view.
A DE subsystem processor generates local histograms of sub-regions, which show separation of the distributions of DE regions and ROI. The local histograms are smoothed using a moving average filter. Subsequently, a searching algorithm finds peaks corresponding to DE regions and a valley point that separates DE regions and ROI. The detected valley point is adjusted with anatomy and the adjusted value is used as a threshold value for the region. The DE sub-regions are segmented with the threshold value. Hole filling and small object removal is then performed to complete the DE segmentation.
Final verification of the collimated, ROI, and DE regions is performed and, assuming they are verified, the final mask is labeled. If all regions are not verified, an error results.